El Niño vs La Niña#
Highlights:
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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import df2img
sys.path.append("../../../../indicators_setup")
from ind_setup.colors import get_df_col, plotting_style
from ind_setup.tables import plot_df_table
from ind_setup.plotting_int import plot_oni_index_th
from ind_setup.plotting import plot_bar_probs_ONI, add_oni_cat
plotting_style()
from ind_setup.core import fontsize
sys.path.append("../../../functions")
from data_downloaders import GHCN, download_oni_index
Define location and variables of interest#
country = 'Palau'
vars_interest = ['PRCP']
Get Data#
https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf
update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"
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if update_data:
df_country = GHCN.get_country_code(country)
print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')
df_stations = GHCN.download_stations_info()
df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
print(f'There are {df_country_stations.shape[0]} stations in {country}')
Using Koror Station#
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if update_data:
GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
id = 'PSW00040309' # Koror Station
dict_prcp = GHCN.extract_dict_data_var(GHCND_dir, 'PRCP', df_country_stations.loc[df_country_stations['ID'] == id])[0]
data = dict_prcp[0]['data']#.dropna()
data.to_pickle(op.join(path_data, 'GHCN_precipitation.pkl'))
else:
data = pd.read_pickle(op.join(path_data, 'GHCN_precipitation.pkl'))
st_data = data
ONI index#
https://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php
p_data = 'https://psl.noaa.gov/data/correlation/oni.data'
if update_data:
df1 = download_oni_index(p_data)
df1.to_pickle(op.join(path_data, 'oni_index.pkl'))
else:
df1 = pd.read_pickle(op.join(path_data, 'oni_index.pkl'))
lims = [-.5, .5]
plot_oni_index_th(df1, lims = lims)
st_data_monthly = st_data.resample('M').mean()
st_data_monthly.index = pd.DatetimeIndex(st_data_monthly.index).to_period('M').to_timestamp() + pd.offsets.MonthBegin(1)
df1['prcp'] = st_data_monthly['PRCP']#.rolling(window=rolling_mean).mean()
df1 = add_oni_cat(df1, lims = lims)
df2 = df1.resample('Y').mean()
fig= plot_bar_probs_ONI(df2, var='prcp', y_label = 'Mean Annual Precipitation')
df2['prcp_ref'] = df2.prcp - df2.loc['1961':'1990'].prcp.mean()
fig = plot_bar_probs_ONI(df2, var='prcp_ref', y_label = 'Precipitation [mm]')
fig.suptitle('Mean Annual Precipitation Anomaly over the 1961-1990 mean', fontsize = fontsize, y = 1.05)
plt.savefig(op.join(path_figs, 'F5_Rain_mean.png'), dpi=300, bbox_inches='tight')
df_format = np.round(df1.describe(), 2)
fig = plot_df_table(df_format, figsize = (400, 300))
df2img.save_dataframe(fig=fig, filename="getting_started.png")